pytorch-patterns

To make PyTorch training reproducible, you must set deterministic seeding and apply specific deterministic flags before running experiments. This ensures that code execution, random weight initialization, and data shuffling yield identical results across multiple runs for accurate comparison.

The best way to write device-agnostic PyTorch code is to avoid hardcoding `.cuda()` calls. Instead, dynamically resolve the device and manage tensor placements explicitly, which improves portability and allows the same training script to run smoothly on both CPU and GPU hardware.

You can optimize GPU memory and throughput during PyTorch training by applying mixed precision and tuning DataLoader settings. Implementing checkpointing best practices also prevents redundant computations and reduces memory overhead during complex model training loops.

Different results in train vs eval mode occur because PyTorch models use correct `train()` and `eval()` semantics to toggle behaviors like dropout and batch normalization. Properly managing these states alongside `no_grad()` ensures accurate inference and gradient tracking during training loops.

To implement safe checkpoint save and load for resuming PyTorch experiments, serialize the model state, optimizer state, and epoch alongside deterministic seeding. This checkpointing workflow allows you to safely pause and resume training pipelines without losing data pipeline progress.

Yes, you can use custom `collate_fn` in your PyTorch DataLoader to fix data pipelines and handle variable-length batches. Combining this with efficient DataLoader settings and explicit shape management ensures robust data loading for complex training loops.